THE FORGE RANKINGS·2026-08-30

Claude Sonnet 4.5

Anthropic
31
RANK of 37 ranked
FERROX INDEX38.8 weighted mean of category positions
GRADED back of the field
EVIDENCE13 / 32 benchmarks measured
QUALITYTHIN Correlated evidence in: preference. Those category scores rest on benchmarks from a single family.

Compared to what

Each rail is one classification. Every ranked model measured on it is a tick; this model is the orange marker. A hatched rail means this model has no measurement in that classification and its weight was redistributed.

Agents

2of 37 measured
53.8
13.1median 38.963.0

Coding

23of 37 measured
47.0
22.4median 50.070.7

Reasoning

32of 37 measured
33.9
18.1median 62.893.0

Preference

13of 15 measured
63.7
53.1median 71.882.5

Every measurement, and the field behind it

One card per benchmark this model has been measured on. The rail shows where it sits against every other ranked model measured on the same benchmark. Where the evaluator keys on a harness, the harness is named, because the same model scores differently under a different one.

ALE-Bench

30/35
796 best 2177 · GPT-5.6 Sol
ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0 32k

SWE-bench Verified (Epoch's own run)

16/21
71.3% best 83.5% · Claude Opus 4.7
Epoch AI CC-BY-4.0

LMArena Text (style-controlled)

12/20
1455 best 1507 · Claude Fable 5
LMArena CC-BY-4.0

LMArena WebDev

15/16
1386 best 1626 · Claude Fable 5
LMArena CC-BY-4.0

METR time horizons

5/11
2.0h best 6.4h · Gemini 3.1 Pro Pr…
METR - Measuring AI Ability to Complete Long Tasks CC-BY-4.0 16k

Not measured

19/32
  • APEX Agents
  • LMArena Agent
  • DeepSWE
  • FrontierCode
  • Terminal-Bench 4.0
  • Aider Polyglot
  • BrowseComp
  • IMOAnswerBench
  • SWE-bench Multilingual
  • AIME
  • CyberGym
  • HMMT
  • MCP-Atlas
  • SWE-bench Pro
  • Tau2-Bench Airline
  • Tau2-Bench Banking
  • Tau2-Bench Retail
  • Tau2-Bench Telecom
  • Tool-Decathlon
A missing benchmark is not a zero and is never scored as one.

Check it yourself

Every number on this page, with who measured it, the interval they published, the harness it was run under, and where to go and read it. Nothing here was measured by Ferrox Labs.

BenchmarkClassPublishedInterval (normalised) Measured byLicenceHarness / effort
Cybench agents 55.0% not published Cybench leaderboard CC-BY-4.0 source
METR time horizons agents 2.0h 50.0 to 68.7 METR - Measuring AI Ability to Complete Long Tasks CC-BY-4.0 / 16k source
OSWorld agents 58.1% not published OSWorld (XLANG Lab) none stated General model / 50 steps source
Terminal-Bench 2 agents 42.6% 37.1 to 48.1 Terminal-Bench v2 Leaderboard CC-BY-4.0 OpenHands source
ALE-Bench coding 796 not published ALE-Bench (Sakana AI with AtCoder) CC-BY-4.0 / 32k source
SWE-bench Verified (Epoch's own run) coding 71.3% 67.3 to 75.3 Epoch AI CC-BY-4.0 source
LMArena Text (style-controlled) preference 1455 78.9 to 79.7 LMArena CC-BY-4.0 source
LMArena WebDev preference 1386 47.3 to 49.1 LMArena CC-BY-4.0 source
ARC-AGI-2 reasoning 6.9% not published ARC Prize CC-BY-4.0 / 16k source
FrontierMath reasoning 13.5% 9.6 to 17.4 Epoch AI CC-BY-4.0 / 59k source
GPQA Diamond (Epoch's own run) reasoning 78.8% 73.1 to 84.5 Epoch AI CC-BY-4.0 / 16k source
Humanity's Last Exam reasoning 7.5% 5.5 to 9.5 Humanity’s Last Exam (CAIS / Scale AI) CC-BY-4.0 source
OTIS Mock AIME 2024-2025 reasoning 71.1% 57.7 to 84.5 Epoch AI CC-BY-4.0 / 16k source

Configurations rolled up: 14. Rule: median observed configuration per benchmark (lower median, always a real measurement). Harnesses seen: claude-code, general-model, maya-v2, mini-swe-agent, openhands, terminus-2.

Snapshot 2026-08-30-62e4e85f043b, manifest dc46131315046f1e. Grades are positional: position in the measured field, as a percentile of rank among ranked entries, n=37.